Merge branch 'dev' into reorder-hotkeys

1 files changed, 15 insertions, 12 deletions

diff --git a/modules/prompt_parser.py b/modules/prompt_parser.py
index 69665372..b4aff704 100644
--- a/modules/prompt_parser.py
+++ b/modules/prompt_parser.py
@@ -54,18 +54,21 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     """

 

     def collect_steps(steps, tree):

-        l = [steps]

+        res = [steps]

+

         class CollectSteps(lark.Visitor):

             def scheduled(self, tree):

                 tree.children[-1] = float(tree.children[-1])

                 if tree.children[-1] < 1:

                     tree.children[-1] *= steps

                 tree.children[-1] = min(steps, int(tree.children[-1]))

-                l.append(tree.children[-1])

+                res.append(tree.children[-1])

+

             def alternate(self, tree):

-                l.extend(range(1, steps+1))

+                res.extend(range(1, steps+1))

+

         CollectSteps().visit(tree)

-        return sorted(set(l))

+        return sorted(set(res))

 

     def at_step(step, tree):

         class AtStep(lark.Transformer):

@@ -92,7 +95,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     def get_schedule(prompt):

         try:

             tree = schedule_parser.parse(prompt)

-        except lark.exceptions.LarkError as e:

+        except lark.exceptions.LarkError:

             if 0:

                 import traceback

                 traceback.print_exc()

@@ -140,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
         conds = model.get_learned_conditioning(texts)

 

         cond_schedule = []

-        for i, (end_at_step, text) in enumerate(prompt_schedule):

+        for i, (end_at_step, _) in enumerate(prompt_schedule):

             cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))

 

         cache[prompt] = cond_schedule

@@ -216,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)

     for i, cond_schedule in enumerate(c):

         target_index = 0

-        for current, (end_at, cond) in enumerate(cond_schedule):

-            if current_step <= end_at:

+        for current, entry in enumerate(cond_schedule):

+            if current_step <= entry.end_at_step:

                 target_index = current

                 break

         res[i] = cond_schedule[target_index].cond

@@ -231,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
     tensors = []

     conds_list = []

 

-    for batch_no, composable_prompts in enumerate(c.batch):

+    for composable_prompts in c.batch:

         conds_for_batch = []

 

-        for cond_index, composable_prompt in enumerate(composable_prompts):

+        for composable_prompt in composable_prompts:

             target_index = 0

-            for current, (end_at, cond) in enumerate(composable_prompt.schedules):

-                if current_step <= end_at:

+            for current, entry in enumerate(composable_prompt.schedules):

+                if current_step <= entry.end_at_step:

                     target_index = current

                     break